Deep Learning and Generative Artificial Intelligence

Deep Learning and Generative Artificial Intelligence, available at $54.99, has an average rating of 4.82, with 181 lectures, based on 17 reviews, and has 467 subscribers.

You will learn about Learn the basic principles of artificial neural networks and how they are trained. Implement and train Convolutional Neural Networks (CNNs) for image classification and object detection using Python. Design and apply Long Short-Term Memory (LSTM) networks to predict and analyze time series data. Construct, fine-tune, and deploy Transformer models, such as GPT-type models, for various natural language processing tasks. Create and train Generative Adversarial Networks (GANs) to generate realistic synthetic images and data. Build and utilize Variational Auto-Encoders (VAEs) for data compression and generation tasks. Apply style transfer and stable diffusion methods to creatively alter and enhance images. This course is ideal for individuals who are This course is designed for anyone interested in deep learning and generative AI, including beginners with no programming experience who want to use AI through user-friendly interfaces, as well as programmers looking to deepen their understanding and skills in this field. It is particularly useful for This course is designed for anyone interested in deep learning and generative AI, including beginners with no programming experience who want to use AI through user-friendly interfaces, as well as programmers looking to deepen their understanding and skills in this field.

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Summary

Title: Deep Learning and Generative Artificial Intelligence

Price: $54.99

Average Rating: 4.82

Number of Lectures: 181

Number of Published Lectures: 181

Number of Curriculum Items: 181

Number of Published Curriculum Objects: 181

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Learn the basic principles of artificial neural networks and how they are trained.

Implement and train Convolutional Neural Networks (CNNs) for image classification and object detection using Python.

Design and apply Long Short-Term Memory (LSTM) networks to predict and analyze time series data.

Construct, fine-tune, and deploy Transformer models, such as GPT-type models, for various natural language processing tasks.

Create and train Generative Adversarial Networks (GANs) to generate realistic synthetic images and data.

Build and utilize Variational Auto-Encoders (VAEs) for data compression and generation tasks.

Apply style transfer and stable diffusion methods to creatively alter and enhance images.

Who Should Attend

This course is designed for anyone interested in deep learning and generative AI, including beginners with no programming experience who want to use AI through user-friendly interfaces, as well as programmers looking to deepen their understanding and skills in this field.

Target Audiences

This course is designed for anyone interested in deep learning and generative AI, including beginners with no programming experience who want to use AI through user-friendly interfaces, as well as programmers looking to deepen their understanding and skills in this field.

Welcome to the Deep Learning and Generative Artificial Intelligence course! This comprehensive course is designed for anyone interested in diving into the exciting world of deep learning and generative AI, whether you’re a beginner with no programming experience or an experienced developer looking to expand your skill set.

What You Will Learn:

Foundations of Deep Learning and Artificial Neural Networks: Gain a solid understanding of the basic concepts and architectures that form the backbone of modern AI.

Convolutional Neural Networks (CNNs): Learn how to implement and train CNNs for image classification and object detection tasks using Python and popular deep learning libraries.

Long Short-Term Memory (LSTM) Networks: Explore the application of LSTM networks to predict and analyze time series data, enhancing your ability to handle sequential data.

Transformer Models: Dive into the world of Transformer models, including GPT-type models, and learn how to construct, fine-tune, and deploy these models for various natural language processing tasks.

Generative Adversarial Networks (GANs): Understand the principles behind GANs and learn how to create and train them to generate realistic synthetic images and data.

Variational Auto-Encoders (VAEs): Discover how to build and utilize VAEs for data compression and generation, understanding their applications and advantages.

Style Transfer and Stable Diffusion: Experiment with style transfer techniques and stable diffusion methods to creatively alter and enhance images.

Course Features:

Interactive Coding Exercises: Engage with hands-on coding exercises designed to reinforce learning and build practical skills.

User-Friendly Demos and Playgrounds: For those who prefer a more visual and interactive approach, our course includes demos and playgrounds to experiment with AI models without needing to write code.

Real-World Examples: Each module includes real-world examples and case studies to illustrate how these techniques are applied in various industries.

Project-Based Learning: Apply what you’ve learned by working on projects that mimic real-world scenarios, allowing you to build a portfolio of AI projects.

Who Should Take This Course?

Aspiring AI Enthusiasts: Individuals with no prior programming experience who want to understand and leverage AI through intuitive interfaces.

Developers and Data Scientists: Professionals looking to deepen their understanding of deep learning and generative AI techniques.

Students and Researchers: Learners who want to explore the cutting-edge advancements in AI and apply them to their studies or research projects.

Course Curriculum

Chapter 1: Foundations of Modern AI

Lecture 1: Foundations 01

Lecture 2: Foundations 02

Lecture 3: Foundations 03

Lecture 4: Foundations 04

Lecture 5: Foundations 05

Lecture 6: Foundations 06

Lecture 7: Foundations 07

Lecture 8: Foundations 08

Lecture 9: Foundations 09

Lecture 10: Foundations 10

Lecture 11: Foundations 11

Lecture 12: Foundations 12

Lecture 13: Foundations 13

Lecture 14: Foundations 14

Lecture 15: Foundations 15

Lecture 16: Foundations 16

Lecture 17: Foundations 17

Lecture 18: Foundations 18

Lecture 19: Foundations 19

Lecture 20: Foundations 20

Lecture 21: Foundations 21

Lecture 22: Foundations 22

Lecture 23: Foundations 23

Lecture 24: Foundations 24

Lecture 25: Foundations 25

Lecture 26: Foundations 26

Lecture 27: Foundations 27

Lecture 28: Foundations 28

Lecture 29: Foundations 29

Lecture 30: Foundations 30

Lecture 31: Foundations 31

Lecture 32: Foundations 32

Lecture 33: Foundations 33

Lecture 34: Foundations 34

Lecture 35: Foundations 35

Lecture 36: Foundations 36

Lecture 37: Foundations 37

Lecture 38: Foundations 38

Lecture 39: Foundations 39

Lecture 40: Foundations 40

Lecture 41: Foundations 41

Lecture 42: Foundations 42

Chapter 2: Playground for the Foundational Part of the Course

Lecture 1: Neural Network Playground

Chapter 3: Code demos for the Foundational Part of the Course

Lecture 1: Introduction to the Course Code Repository (on GitHub)

Lecture 2: Example of Backpropagation

Lecture 3: Tradicional (Fully Connected) Neural Network versus CNN

Chapter 4: Artificial Intelligence for Visual Tasks

Lecture 1: AI for Vision – Part 01

Lecture 2: AI for Vision – Part 02

Lecture 3: AI for Vision – Part 03

Lecture 4: AI for Vision – Part 04

Lecture 5: AI for Vision – Part 05

Lecture 6: AI for Vision – Part 06

Lecture 7: AI for Vision – Part 07

Lecture 8: AI for Vision – Part 08

Lecture 9: AI for Vision – Part 09

Lecture 10: AI for Vision – Part 10

Lecture 11: AI for Vision – Part 11

Lecture 12: AI for Vision – Part 12

Lecture 13: AI for Vision – Part 13

Lecture 14: AI for Vision – Part 14

Lecture 15: AI for Vision – Part 15

Lecture 16: AI for Vision – Part 16

Lecture 17: AI for Vision – Part 17

Lecture 18: AI for Vision – Part 18

Lecture 19: AI for Vision – Part 19

Lecture 20: AI for Vision – Part 20

Lecture 21: AI for Vision – Part 21

Lecture 22: AI for Vision – Part 22

Lecture 23: AI for Vision – Part 23

Lecture 24: AI for Vision – Part 24

Lecture 25: AI for Vision – Part 25

Lecture 26: AI for Vision – Part 26

Lecture 27: AI for Vision – Part 27

Lecture 28: AI for Vision – Part 28

Lecture 29: AI for Vision – Part 29

Lecture 30: AI for Vision – Part 30

Chapter 5: Playgrounds for AI for Vision

Lecture 1: CNN Playground 01

Lecture 2: CNN Playground 02

Chapter 6: Code demos of AI for Computer Vision

Lecture 1: Code demo 1

Lecture 2: Code demo 2

Lecture 3: Code demo 3

Chapter 7: Deep Learning for Time Series

Lecture 1: Deep Learning for Time Series 01

Lecture 2: Deep Learning for Time Series 02

Lecture 3: Deep Learning for Time Series 03

Lecture 4: Deep Learning for Time Series 04

Lecture 5: Deep Learning for Time Series 05

Lecture 6: Deep Learning for Time Series 06

Lecture 7: Deep Learning for Time Series 07

Lecture 8: Deep Learning for Time Series 08

Lecture 9: Deep Learning for Time Series 09

Lecture 10: Deep Learning for Time Series 10

Lecture 11: Deep Learning for Time Series 11

Lecture 12: Deep Learning for Time Series 12

Instructors

Luís Cunha, PhD
University Professor

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2 stars: 1 votes

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4 stars: 0 votes

5 stars: 16 votes

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